A Dataset and Benchmark Towards Multi-Modal Face Anti-Spoofing Under Surveillance Scenarios

Abstract

Face Anti-spoofing (FAS) is a challenging problem due to complex serving scenarios and diverse face presentation attack patterns. Especially when captured images are low-resolution, blurry, and coming from different domains, the performance of FAS will degrade significantly. The existing multi-modal FAS datasets rarely pay attention to the cross-domain problems under deployment scenarios, which is not conducive to the study of model performance. To solve these problems, we explore the fine-grained differences between multi-modal cameras and construct a cross-domain multi-modal FAS dataset under surveillance scenarios called GREAT-FASD-S. Besides, we propose an Attention based Face Anti-spoofing network with Feature Augment (AFA) to solve the FAS towards low-quality face images. It consists of the depthwise separable attention module (DAM) and the multi-modal based feature augment module (MFAM). Our model can achieve state-of-the-art performance on the CASIA-SURF dataset and our proposed GREAT-FASD-S dataset.

arXiv commentPublished in: IEEE Access

References in corpus (11)

• PyTorch: An Imperative Style, High-Performance Deep Learning Library
• Neural Machine Translation by Jointly Learning to Align and Translate
• MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
• Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks
• Unsupervised Domain Adaptation by Backpropagation
• Squeeze-and-Excitation Networks
• GANFIT: Generative Adversarial Network Fitting for High Fidelity 3D Face Reconstruction
• Learning Generalized Spoof Cues for Face Anti-spoofing
• Support Vector Guided Softmax Loss for Face Recognition
• Multi-Modal Face Anti-Spoofing Based on Central Difference Networks
• CASIA-SURF CeFA: A Benchmark for Multi-modal Cross-ethnicity Face Anti-spoofing